Data Mining: Ethical, Security, and Privacy Implications in Business

This report delves into the utilization of data mining in the business sector, highlighting its significance in extracting valuable insights from vast datasets. It explores the practical applications of data mining, emphasizing its role in enhancing business strategies and gaining a competitive edge. The report then pivots to address the critical ethical, security, and privacy implications associated with data mining. It examines ethical dilemmas, such as data privacy and accuracy, and discusses the importance of responsible data handling. The analysis further covers security concerns, including unauthorized data access, and privacy implications, like the potential for misuse of personal information. Finally, the report underscores the importance of establishing robust authorization rules and guidelines to mitigate these risks, safeguarding both business operations and customer data. The references include the sources from where the report has been compiled.

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Data Mining
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Table of Content
Part 1..............................................................................................................................3
Data Mining is utilized in Business........................................................................3
Summary of Article................................................................................................4
Part 2..............................................................................................................................5
Leading ethical, security, and privacy implications in DM...................................5
Implications are significant for the Business Sector..............................................7
References......................................................................................................................9

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Part 1
Data Mining is utilized in Business
Over the previous years, Information Mining and Data Mining (DM) has turned into a
matter that is of extensive significance because of the huge amount of information or
data accessible in the different applications which belongs to different domains. DM,
is used in business enterprises as it is a dynamic and a quick extending field that
simply applies propelled data examination strategies, from machine learning,
statistics, database frameworks or AI to easily find out the significant examples,
relations and patterns contained in the data, and the data that is difficult to observe by
utilizing different systems. DM in business perspective is characterized as a business
procedure for investigating a lot of information or data to find meaningful rules and
patterns. Business organizations can apply DM with a specific end goal to enhance
their business as well as gain advantages over their competitors (Wang & Wang,
2014). On the other hand, DM tools are monetarily or commercially accessible to
execute different data mining procedures for performing propelled data examination
on substantial volumes of information (Tasioulas, 2016). To move simply from
quality control management to quality certainty and to decrease the error occurrence,
business enterprises need to utilize their active knowledge and former experiences
more viably. Data mining investigation or analysis offers several potential profits in
this specific context. The DM can also help to achieve competitive improvements,
such as, increasing or improving the product quality and reducing the production cost
or time (Żytkow & Rauch, 2014).

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Summary of Article
Article: Data Mining and its Relevance to Business
Retrieved From:
http://analyticstraining.com/2016/data-mining-and-its-relevance-to-business/
In this article, the author summarizes that Data Mining (DM) uses a well-defined or a
well-constituted statistical and machine learning methods to anticipate the customer
behaviour with regards to business perspective. This technique or methodology can be
utilized for both, searching analysis and prophetical modelling. The data mining
procedure is not independent of the enterprise business procedure. The influence of
DM can be entangled only when there are lots of influences on the process of
business. Competitiveness progressively relies upon enhancing the nature of basic
leadership from the past data ("Data Mining And Its Relevance To Business", 2016).
DM, when used in business, enhances the knowledge of building capabilities and
services and products empowers the business specialists to appropriately target the
upcoming production strategies or procedures. Therefore, data mining requires to
have a connection to the inherent business procedures. In this article, the author also
explains that Data mining has become a very essential tool for all business processes.
Nowadays, technology has reinforced to store a huge volume of information as well
as data, unlike a period of time ago, where several businesses consider the
accumulation of data as an uneconomical expenditure. As DM is the subject which is
changing more quickly with fresh technologies and ideas continually under
academicians, so, the developers, researchers as well as the experts on the subject
require continuous access to the current data about the ideas, issues, technologies and
trends in this rising field.

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Part 2
Leading ethical, security, and privacy implications in DM
Ethical implications: There are lots of moral or ethical implications of data mining,
such as, privacy of data, accuracy of data, security of data, legal liability of data,
database security. It ought to be very clear that the DM should not at all be an
ethically problematic issue. All ethical dilemmas emerge when the mining of personal
data is executed. Ethical implications, such as, interdisciplinary frameworks as well as
solution services are just like a critical origin of information affiliated with rising
issues and possible solutions in the DM and also the impact of economic and political
factors. A huge breakthrough, this basic reference gives succinct scope of arising
issues and innovative arrangements in DM, and simply covers the issues with
appropriate laws (Leung, 2011).
For example, in a manufacturing industry, the mining of data is probably not going to
prompt any outcomes of personally objectionable or frightful nature. Nevertheless,
mining at a very click course of data has been acquired from an inattentive Internet
individual that stimulates a mixture of ethical issues. Maybe the most quickly obvious
reaction of this is the attack on the security, privacy and accuracy of data that is used
in the manufacturing industry.
An organization confronts a moral situation when they became unable to decide that it
should make all the individuals aware about the data being stored for the future
information or data mining (Tasioulas, 2016). As by providing an alternative to an
individual that he/she will opt out or quit from the collection of data, then the
organization badly hurts its own competitive advantages within the market. In that
case, the company must realize that because of these ethical issues, they can face loss

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in their goods & services and customers also. There are a lot of implications for
solving these ethical situations (Verducci, 2012).
For example, an organization which utilizes the data mining methods must act very
responsibly just by monitoring all the ethical issues or problems that are
encompassing their specific application; they should likewise consider the shrewdness
like what their managers and employees are doing. For instance, DM sometimes can
be utilized to separate individuals, particularly, with respect to sexual, racial, and
religious introductions. The utilization of DM in this manner is viewed as an
exploitative as well as illegal (Tasioulas, 2016). People should be shielded from any
untrustworthy utilization of their own data, and before they settle on any choice to
give their information, they should have to know how this data will be utilized, why
this data is being used, what specific parts of that data or information will be taken,
and what outcomes this activity will have. Simply after doing this, all the individuals
must be informed, straightforward about the consequences and the reasons for
utilizing their personal data.
Privacy and Security Implications: In a moral sense, data security is firmly
identified with privacy and this is just because data security represses the unapproved
spread of individual information, thus, additionally improving, though by implication,
a person's ability to manage access to its delicate information. The implications of
DM enables everyone to discover data that are not expected to find out in the
databases. The approval of data or information quality management techniques by the
business firms, combined with the convenient amendment of any mistakes announced
by people and irregular information cleansing might go in some manner to settle the
privacy and security issues in data mining (Reshmy & Paulraj, 2017). The other
possible solutions are obvious, but they might have disappointing implications for the

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security and privacy protection. For consumers or customers, it is very critical to ask
for or demand any high level security or privacy process through vehicles, for
instance, terms and conditions, SLAs, and privacy and security trust stamps from
firms that are collecting and utilizing the big data or data mining (Ryoo, 2016).
Countermeasures, for instance, encryption, control of access, invasion detection,
auditing, backups, and corporate operations can forestall the data from any breach. As
such, privacy and security can easily be promoted with the help of big data which is
one of the best implications for data mining. One major implication is to maintain a
dynamic relation between the human rights and the big data within the business.One
way to do this is to ease a few tensions by asking all the individuals that they are
inclined to attempt privacy or security risks or dangers to contribute or lend to the
progression of technological knowledge (Tasioulas, 2016).
Implications are significant for the Business Sector
Data mining implications, usually embedded in bigger learning discovery processes as
well as systems, are mechanized by logical instruments that have experienced a vast
increase in usage. Data mining implications combine all the disciplines or orders of
statistics, machine learning, databases, as well as data visualization to impact the
analysis of complex and huge data sets. The main goal of DM’s ethical, security and
privacy implications is to uncover the previously unknown designs and connections in
data or information and to introduce the potentially intriguing principles that might
give a helpful insight or a huge competitive benefit (Mikut & Reischl, 2011).
Knowledge Discovery and Data Mining tasks are generally classified into categories,
such as: predictive and descriptive (Ryoo, 2016). With the help of these data mining
implications, authorization rules and guidelines are created within the business firms
so that some restrictions should be there for accessing, using, viewing, and deleting

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data. Implications and authorization principles and rules are utilized to defend the data
or information from all the ethical, security, and privacy issues (Stine, 2011). It
likewise protects the individual data of employee working within the business firm as
well as customers’ information, such as, credit card details and home address details
stored in the database, therefore, maintaining the reputation of the business in front of
the customers (Nimmagadda & Dreher, 2013). It is really essential that honesty
controls and regulations are placed inside the database system so that the information
and data might maintain its security/privacy protection and usefulness. In case,
integrity restraints are not enforced in the database, then, any data or delicate
information of the business that might be created from the business enterprise
database is useless (Tasioulas, 2016). Therefore, all the implications mentioned above
are useful for the business sector. For instance, one of the implications that is any
multi-agent framework in the business sector might utilize is to monitor as well as
supervise all the applications at the actual time. As every user sign in, an operator
would be allotted to control their queries and possibly filter the consequences (Olson,
2016).

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References
Data Mining And Its Relevance To Business. (2016). Retrieved from
http://analyticstraining.com/2016/data-mining-and-its-relevance-to-business/
Leung, C. (2011). Mining uncertain data. Wiley Interdisciplinary Reviews: Data
Mining And Knowledge Discovery, 1(4), 316-329.
Mikut, R., & Reischl, M. (2011). Data mining tools. Wiley Interdisciplinary Reviews:
Data Mining And Knowledge Discovery, 1(5), 431-443.
Nimmagadda, S., & Dreher, H. (2013). Data warehousing and mining technologies
for adaptability in turbulent resources business environments. International
Journal Of Business Intelligence And Data Mining, 6(2), 113.
Olson, D. (2016). Data mining in business services. Service Business, 1(3), 181-193.
Reshmy, A., & Paulraj, D. (2017). Data mining of unstructured big data in cloud
computing. International Journal Of Business Intelligence And Data Mining,
12(3/4), 1.
Ryoo, J. (2016). Big data security problems threaten consumers’ privacy. Retrieved
from http://theconversation.com/big-data-security-problems-threaten-consumers-
privacy-54798
Stine, R. (2011). Making room for the modeler in data mining. Statistical Analysis
And Data Mining, 5(1), 90-91.
Tasioulas, J. (2016). Big Data, Human Rights and the Ethics of Scientific Research.
Retrieved from http://www.abc.net.au/religion/articles/2016/11/30/4584324.htm
Verducci, J. (2012). Prize Winning Papers on Statistical Learning and Data Mining.
Statistical Analysis And Data Mining, 5(6), 477-477.
Wang, H., & Wang, S. (2014). Ontology for Data Mining and its Application to
Mining Incomplete Data. Journal Of Database Management, 19(4), 81-90.

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Ż ytkow, J., & Rauch, J. (2014). Principles of data mining and knowledge discovery.
Berlin: Springer.